Abstract Acoustic Emission Data Research Articles

Structural health monitoring of liquid filled above ground storage tank floors: a time-reversed approach to acoustic emission source location

Acoustic Emission (AE) is a proven successful Non-Destructive Testing (NDT) method to assess the state of storage tank floors. Traditional AE source location in tanks floors is performed using only the wavefronts that have traveled directly from the source to the sensor (direct hit). The wavefronts captured after reflected from the tank walls are identified and discarded. This paper proposes a new AE source location algorithm in tanks that considers a combination of reflections and direct hits. The proposed algorithm is based on time-reversed acoustics and ray theory. The methodology uses the concept of time-reversed acoustics in which a wave detected at any location can be directed back to the source when re-created at the detection place. Therefore, the developed approach takes the time at which each wave arrives to the sensor and sends it back as if time had reversed. Ray theory is used in the methodology to account for the way in which the wavefront is reflected when encounters an obstacle such as the walls of the tank. Then, the point of intersection of all wavefronts is identified using an optimization algorithm. This point where all wavefronts intersect is considered the location of the source. The location algorithm considers the first path or direct hits from the source to the sensors combined with reflections obtained by wavefronts bouncing from the tank walls. The proposed location algorithm was validated using numerical data from 176ft diameter tank and experimentally using AE data from a tank 55ft diameter.

Analysis of Acoustic Emission Propagation in Metal-to-Metal Adhesively Bonded Joints

Acoustic emission (AE) monitoring shows promise as one of the most effective methods for condition monitoring of adhesively-bonded joints. Previous research has demonstrated its ability to detect, locate and classify adhesive joint failure, though in these studies little attention appears to have been paid to the differences in AE wave propagation through the bonded and un-bonded sections of the specimens tested, or to the effects of the wave modes excited or the propagation distances. This paper details an experimental study conducted on large aluminium sheet specimens to identify the effects of the presence of an adhesive layer on AE wave propagation. Three specimens are considered; a single aluminium sheet, two aluminium sheets placed together without adhesive, and an adhesively-bonded specimen. A pencil lead break (PLB) is used as a simulated AE source, and is applied to the three specimens at varying propagation distances and orientations. The acquired signals are processed using wavelet-transforms to explore time-frequency features, and compared with modified group-velocity curves based on the Rayleigh–Lamb equations to allow identification of wave-modes and edge-reflections. The effects of propagation distance and source orientation are investigated while comparison is made between the three specimens. It is concluded that while the wave propagation modes can be approximated as being constant throughout all three specimens, there is a significant change in the received waveforms due to the attenuation of high-frequency components exhibited by the bonded specimen. These findings may be utilised to provide a deeper understanding of acquired AE data, improving the current abilities to identify, locate and characterise damage mechanisms occurring within adhesive joints, ultimately improving safety in the use of adhesive bonding for critical applications.

A Strategy to Determine Partial Discharge in XLPE Power Cables Using Acoustic Emission Detection Technique

Abstract Acoustic emission (AE) is a nondestructive technique that has unique potential for evaluating degradation and possibly for failure prediction, as well, of cross-linked polyethylene (XLPE) cables. This technique is more suited in high-voltage tests because of its external antielectromagnetism interference. The authors have demonstrated its capability toward failure prediction on XLPE cables. In this study, five sets of fifty identical XLPE cables were taken up for the investigation. These tests were carried out on both damaged and undamaged conditions. The AE data acquired during loading cycles is analyzed, and a lucid empirical relation is developed to predict their failure performance. Even though there is no pinpoint method spelled out in the open literature for the failure prediction of XLPE cables, one could appreciate the approach that impending failure is significant even at 50–60 % of maximum expected operating voltage with a reasonable error margin. Moreover, this methodology can also be applied to predict in real time the failure of similar XLPE cables made of other material systems.

Predicting Failure Strength of Randomly Oriented Short Glass Fiber-Epoxy Composite Specimen by Artificial Neural Network Using Acoustic Emission Parameters

Acoustic emission (AE) peak amplitude and cumulative energy emitted during 50% of failure of composite specimen was collected, analyzed, and utilized to predict the ultimate tensile strength (UTS) using artificial neural network (ANN) and the performance of various training algorithm on prediction was analyzed. AE data have been collected from finite numbers of randomly oriented short glass fiber-epoxy tensile specimens, while loading up to failure in a tensile testing machine. AE response from each of the specimen was classified and segregated by understanding the failure mechanism. A feed forward back-propagation type ANN was designed and the segregated data of amplitude hits and cumulative energy was processed using two separate networks to predict the UTS of corresponding specimens using it with appropriate parameters and the results were analyzed.

The specific features of acoustic-emission testing of vessel equipment with a wall delamination of a technological origin

Acoustic-emission (AE) signals from the zones of delaminations in vessel walls of technological origin are analyzed via comparison to data that were obtained by the ultrasonic method and strength calculations. The boundaries of the detectability of a delamination-type flaw using the AE technique are established in accordance with the regulatory engineering documentation that is in force. A method for determining the dimensions, location depth, and opening width of a delamination using AE data and simple calculated dependences, which are applicable under testing conditions, is proposed.

Identification of damage mechanisms in self-reinforced polyethylene composites by using pattern recognition techniques on AE data

Acoustic emission (AE) signals collected from thermoplastic self-reinforced polyethylene composites ultra-high molecular weight polyethylene fibre reinforced low-density polyethylene (UHMWPE/LDPE) under quasi-static tensile load were clustered and identified by unsupervised pattern recognition (UPR) and supervised pattern recognition (SPR) techniques in order to clarify various damage modes in the composites. The purpose was to find an easy way to separate a set of data with a large number of unknown AE signals into several classes attributed to a specific damage mode each. UPR techniques were utilised first to classify the AE signals from simple lay-up laminate specimens automatically and mathematically. Different damage modes were identified and a physical validation was carried out by the scanning electron microscope (SEM) technique. Damage investigation of the specimen according to the clustering results showed reasonable results. Therefore, the labelling data set consisting of signals from different damage modes was used as the reference for a SPR system. A large number of AE signals from quasi-isotropic laminates were then identified by the supervised method. It showed good results, which were also supported by the SEM examination. A reliable and convenient procedure was established for a good identification of large number of unknown AE data for the UHMWPE/LDPE composites. This methodology is promising for any other fibre reinforced composites in the field of damage mechanisms analysis.

English

Acoustic emission (AE) monitoring is carried out during proof pressure testing of pressurevessels to find the occurrence of any crack growth-related phenomenon. While carrying out AEmonitoring, it is often found that the background noise is very high. Along with the noise, thesignal includes various phenomena related to crack growth, rubbing of fasteners, leaks, etc. Dueto the presence of noise, it becomes difficult to identify signature of the original signals related to the above phenomenon. Through various filtering/ thresholding techniques, it was found that the original signals were getting filtered out along with noise. Wavelet transformation technique is found to be more appropriate to analyse the AE signals under such situations. Wavelet transformation technique is used to de-noise the AE data. The de-noised signal is classified to identify a signature based on the type of phenomena.Defence Science Journal, 2008, 58(4), pp.559-564, DOI:http://dx.doi.org/10.14429/dsj.58.1677

Acoustic Emission Parameters Dependence on the Destruction Process Characteristics into Concrete

Acoustic emission (AE) techniques are extensively applied to a variety of fields including civil engineering. Now a new generation of AE techniques, which are able to register and process AE data in real-time, has appeared. Still, there exist some problems to correctly interpreting these data and to completely understand the meaning of AE parameters. In order to solve this, several AE waveform and spectral parameters are investigated, based on an experimental study of concrete and reinforced concrete structures.

Low Proof Load Prediction of Ultimate Loads of Fiberglass/Epoxy Resin I-Beams Using Acoustic Emission

Abstract Acoustic emission (AE) nondestructive testing was used to monitor fiberglass/epoxy I-beams. The experiment consisted of loading the I-beams in cantilever fashion with a hydraulic ram. While testing, AE waveforms were collected from the onset of loading to failure. After acquisition, the AE data from each test were filtered to include only data collected up to 50 % of the theoretical ultimate load for further analysis. A Kohonen self-organizing map (SOM) was utilized to separate individual data points into failure mechanism clusters. Then a multiple linear regression analysis was performed using the percentage of hits associated with each failure mechanism along with the epoxy type to develop a prediction equation. The results of this analysis provided a prediction to within a 36.0 % error. A second analysis was performed utilizing a back-propagation neural network. The inputs to the network included a categorical variable for the epoxy type together with the amplitude frequencies from 30–100 dB. The optimized network contained two hidden layers having nine neurons apiece. Here the ultimate load prediction was within 48 lbf for a 9.5 % error. Thus, the back-propagation neural network produced far better results than the SOM/multiple linear regression, probably because of unwanted noise and perhaps nonlinearities in the data.